Introducing the War Map Framework. A Causal-Operational Formalism for Disease Reversal, Sub-Regressor Architecture, and Mechanistic Cure Verification

Abstract
This manuscript introduces a formal language for representing chronic disease as a multiscale causal-operational graph. While chronic diseases are usually diagnosed through downstream clinical criteria and managed through therapies that control markers of disease expression, such control does not by itself specify what would count as mechanistic reversal. The War Map Framework proposes a formal architecture for disease-reversal science. 

Key Framework Components

          1. The War Map: This is defined as a minimal, ancestry-closed, typed, evidence-weighted directed multigraph. It contains the target pathology, its sub-regressors, measurement operators, intervention policies, effect weights, credibility scores, context domains, and update rules.
          2. Sub-regressors: The architecture decomposes target pathologies into biological state variables called sub-regressors, which are target-relative, observable, modifiable, non-constitutive, and causally material.
          3. Mechanistic Cure Verification: The framework defines a cure certificate over the target state and its essential upstream sub-regressors, rather than relying on downstream biomarkers alone.

Objective and Scope The aim of this framework is to turn "root cause medicine" into a computable ontology—a grammar for asking which biological state variables must be measured, modified, and verified before one can claim disease reversal rather than palliative management. This is a conceptual and methodological preprint. It is not a clinical guideline, does not establish empirical validity by itself, and should be interpreted as a proposed framework for research design, grant evaluation, and mechanistic hypothesis generation.

Keywords War Map; sub-regressor; disease reversal; mechanistic cure; structural causal model; dynamic causal system; network medicine; disease maps; digital twin; systems biology; causal inference.